SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Showing 44764500 of 10420 papers

TitleStatusHype
Deep PCB To COCO ConvertorCode2
AugStatic - A Light-Weight Image Augmentation LibraryCode0
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset0
Uncertainty Estimation of Transformer Predictions for Misclassification DetectionCode0
Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural NetworksCode0
DIRA: Dynamic Domain Incremental Regularised AdaptationCode0
NeuralEF: Deconstructing Kernels by Deep Neural NetworksCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
CLIP-Art: Contrastive Pre-training for Fine-Grained Art ClassificationCode2
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining0
Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma0
Unlocking High-Accuracy Differentially Private Image Classification through ScaleCode1
Depth Estimation with Simplified Transformer0
Semantic Information Recovery in Wireless NetworksCode1
Learning to Split for Automatic Bias DetectionCode1
Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor0
One-shot Federated Learning without Server-side TrainingCode0
Understanding The Robustness in Vision TransformersCode2
Quantum-classical convolutional neural networks in radiological image classification0
Causal Transportability for Visual RecognitionCode1
PolyLoss: A Polynomial Expansion Perspective of Classification Loss FunctionsCode1
Brain Tumor Detection and Classification Using a New Evolutionary Convolutional Neural Network0
Adaptive Split-Fusion TransformerCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5DaViT-HTop 1 Accuracy90.2Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90Unverified